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首页> 外文期刊>The Aeronautical Journal >Classification of UAV and bird target in low-altitude airspace with surveillance radar data
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Classification of UAV and bird target in low-altitude airspace with surveillance radar data

机译：利用监视雷达数据对低空空域的无人机和鸟类目标进行分类

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摘要

In order to ensure low-altitude safety, a tracking and recognition method of unmanned aerial vehicle (UAV) and bird targets based on traditional surveillance radar data is proposed. First, several motion models for UAV and flying bird targets are established. Second, the target trajectories are filtered and smoothed with multiple motion models. Third, by calculating the time-domain variance of the model occurrence probability, the model conversion probability of the target is estimated, and then the target type is identified and classified. The effectiveness and robustness of the algorithm is demonstrated by several groups of Monte Carlo simulation experiments, including setting different recognition steps, different model transformation probability, filtering and smoothing algorithm comparison. The algorithm is also successfully applied on the ground-truth radar data collected by the low-altitude surveillance radar at airport and coastal environments, where the targets of UAVs and flying birds could be tracked and recognised.
机译：为了保证低空安全性，提出了一种基于传统监视雷达数据的无人机和鸟目标的跟踪识别方法。首先，建立了无人机和飞鸟目标的几种运动模型。其次，使用多个运动模型对目标轨迹进行过滤和平滑。第三，通过计算模型发生概率的时域方差，估计目标的模型转换概率，然后对目标类型进行识别和分类。通过几组蒙特卡洛模拟实验证明了该算法的有效性和鲁棒性，包括设置不同的识别步骤，不同的模型变换概率，滤波和平滑算法比较。该算法还成功地应用于低空监视雷达在机场和沿海环境中收集的地面真相雷达数据，从而可以跟踪和识别无人机和飞行鸟类的目标。

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